{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://osloyi5le.bkt.clouddn.com/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%B7%A5%E7%A8%8B%E5%B8%88banner.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 朴素贝叶斯算法的Python实现\n",
    "\n",
    "##### by 加号, 网易云课堂 X 稀牛学院 机器学习工程师 附加课件\n",
    "\n",
    "我们会在这里主要讲解如何使用python从0开始写出一套朴素贝叶斯算法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入要用的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "伪造一些数据出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Foot_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>6.00</td>\n",
       "      <td>180</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>5.92</td>\n",
       "      <td>190</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>5.58</td>\n",
       "      <td>170</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>5.92</td>\n",
       "      <td>165</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>5.00</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>female</td>\n",
       "      <td>5.50</td>\n",
       "      <td>150</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>female</td>\n",
       "      <td>5.42</td>\n",
       "      <td>130</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>female</td>\n",
       "      <td>5.75</td>\n",
       "      <td>150</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Height  Weight  Foot_Size\n",
       "0    male    6.00     180         12\n",
       "1    male    5.92     190         11\n",
       "2    male    5.58     170         12\n",
       "3    male    5.92     165         10\n",
       "4  female    5.00     100          6\n",
       "5  female    5.50     150          8\n",
       "6  female    5.42     130          7\n",
       "7  female    5.75     150          9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 新建一个DataFrame\n",
    "data = pd.DataFrame()\n",
    "\n",
    "# 把Y定义好\n",
    "data['Gender'] = ['male','male','male','male','female','female','female','female']\n",
    "\n",
    "# 并给定他们的属性\n",
    "data['Height'] = [6,5.92,5.58,5.92,5,5.5,5.42,5.75]\n",
    "data['Weight'] = [180,190,170,165,100,150,130,150]\n",
    "data['Foot_Size'] = [12,11,12,10,6,8,7,9]\n",
    "\n",
    "# 来看看长什么样子\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再造一个我们要预测的数据点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Foot_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>130</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height  Weight  Foot_Size\n",
       "0       6     130          8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty dataframe\n",
    "person = pd.DataFrame()\n",
    "\n",
    "# Create some feature values for this single row\n",
    "person['Height'] = [6]\n",
    "person['Weight'] = [130]\n",
    "person['Foot_Size'] = [8]\n",
    "\n",
    "# View the data\n",
    "person"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照上课讲的方案，做计数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 男人的总数\n",
    "n_male = data['Gender'][data['Gender'] == 'male'].count()\n",
    "\n",
    "# 女人的总数\n",
    "n_female = data['Gender'][data['Gender'] == 'female'].count()\n",
    "\n",
    "# 全部的人数\n",
    "total_ppl = data['Gender'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算两个最简单的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "P_male = n_male/total_ppl\n",
    "\n",
    "P_female = n_female/total_ppl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算两个指标数据，便于接下来的计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pZPgco6fZBgvFN7ANBhYE94uvk24zui3DC9thL2MNUCbfZj8NMIoO4GPcihHG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\nABNgAkyACTABJsAEmAAT2NIE/n/39Mv/eA8e9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='NB_calculation.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Foot_Size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>5.4175</td>\n",
       "      <td>132.50</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>5.8550</td>\n",
       "      <td>176.25</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Height  Weight  Foot_Size\n",
       "Gender                           \n",
       "female  5.4175  132.50       7.50\n",
       "male    5.8550  176.25      11.25"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_means = data.groupby('Gender').mean()\n",
    "data_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Foot_Size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.097225</td>\n",
       "      <td>558.333333</td>\n",
       "      <td>1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.035033</td>\n",
       "      <td>122.916667</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Height      Weight  Foot_Size\n",
       "Gender                                 \n",
       "female  0.097225  558.333333   1.666667\n",
       "male    0.035033  122.916667   0.916667"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_variance = data.groupby('Gender').var()\n",
    "data_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 男人的Means\n",
    "male_height_mean = data_means['Height'][data_variance.index == 'male'].values[0]\n",
    "male_weight_mean = data_means['Weight'][data_variance.index == 'male'].values[0]\n",
    "male_footsize_mean = data_means['Foot_Size'][data_variance.index == 'male'].values[0]\n",
    "\n",
    "# 男人的Variance\n",
    "male_height_variance = data_variance['Height'][data_variance.index == 'male'].values[0]\n",
    "male_weight_variance = data_variance['Weight'][data_variance.index == 'male'].values[0]\n",
    "male_footsize_variance = data_variance['Foot_Size'][data_variance.index == 'male'].values[0]\n",
    "\n",
    "# 女人的Means\n",
    "female_height_mean = data_means['Height'][data_variance.index == 'female'].values[0]\n",
    "female_weight_mean = data_means['Weight'][data_variance.index == 'female'].values[0]\n",
    "female_footsize_mean = data_means['Foot_Size'][data_variance.index == 'female'].values[0]\n",
    "\n",
    "# 女人的Variance\n",
    "female_height_variance = data_variance['Height'][data_variance.index == 'female'].values[0]\n",
    "female_weight_variance = data_variance['Weight'][data_variance.index == 'female'].values[0]\n",
    "female_footsize_variance = data_variance['Foot_Size'][data_variance.index == 'female'].values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写出 P(x | y)的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def p_x_given_y(x, mean_y, variance_y):\n",
    "\n",
    "    p = 1/(np.sqrt(2*np.pi*variance_y)) * np.exp((-(x-mean_y)**2)/(2*variance_y))\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设测试数据是男人，概率是："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.1970718438780782e-09"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_male * \\\n",
    "p_x_given_y(person['Height'][0], male_height_mean, male_height_variance) * \\\n",
    "p_x_given_y(person['Weight'][0], male_weight_mean, male_weight_variance) * \\\n",
    "p_x_given_y(person['Foot_Size'][0], male_footsize_mean, male_footsize_variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设测试数据是女人，概率是："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00053779091836300176"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_female * \\\n",
    "p_x_given_y(person['Height'][0], female_height_mean, female_height_variance) * \\\n",
    "p_x_given_y(person['Weight'][0], female_weight_mean, female_weight_variance) * \\\n",
    "p_x_given_y(person['Foot_Size'][0], female_footsize_mean, female_footsize_variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比较一下两者，知道该选谁~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
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